CBE-LSE Bibliometric Analysis
Caleb Trujillo, Darcie Nelson, Rachel Kudlacz, Germaine Ng
9/8/2020
##Introduction
There has tremendous growth in the area undergraduate biology research over the last two decades. This study attempts to summarize and analyze the progress of this growth.
Summary of initial data set
This is a placeholder for the inital data set Whole group
txt <- c("rawdata/CBE_LSE_2010-2019/20200717_CBE2010-2019-WOS-1-500.txt",
"rawdata/CBE_LSE_2010-2019/20200717_CBE2010-2019-WOS-501-708.txt")
data <- convert2df(file = txt, dbsource = 'wos', format = "plaintext")##
## Converting your wos collection into a bibliographic dataframe
##
##
## Warning:
## In your file, some mandatory metadata are missing. Bibliometrix functions may not work properly!
##
## Please, take a look at the vignettes:
## - 'Data Importing and Converting' (https://cran.r-project.org/web/packages/bibliometrix/vignettes/Data-Importing-and-Converting.html)
## - 'A brief introduction to bibliometrix' (https://cran.r-project.org/web/packages/bibliometrix/vignettes/bibliometrix-vignette.html)
##
##
## Missing fields: DEDone!
##
##
## Generating affiliation field tag AU_UN from C1: Done!results <- biblioAnalysis(data, sep = ";")options(width=100)
S <- summary(object = results, k = 10, pause = FALSE)##
##
## MAIN INFORMATION ABOUT DATA
##
## Timespan 2010 : 2019
## Sources (Journals, Books, etc) 1
## Documents 708
## Average years from publication 4.97
## Average citations per documents 14.12
## Average citations per year per doc 2.044
## References 18276
##
## DOCUMENT TYPES
## article 656
## correction 18
## letter 30
## review 4
##
## DOCUMENT CONTENTS
## Keywords Plus (ID) 983
## Author's Keywords (DE) 0
##
## AUTHORS
## Authors 2295
## Author Appearances 3255
## Authors of single-authored documents 59
## Authors of multi-authored documents 2236
##
## AUTHORS COLLABORATION
## Single-authored documents 70
## Documents per Author 0.308
## Authors per Document 3.24
## Co-Authors per Documents 4.6
## Collaboration Index 3.5
##
##
## Annual Scientific Production
##
## Year Articles
## 2010 59
## 2011 40
## 2012 50
## 2013 76
## 2014 71
## 2015 58
## 2016 105
## 2017 84
## 2018 84
## 2019 81
##
## Annual Percentage Growth Rate 3.583971
##
##
## Most Productive Authors
##
## Authors Articles Authors Articles Fractionalized
## 1 TANNER KD 23 TANNER KD 10.81
## 2 BROWNELL SE 21 BROWNELL SE 5.92
## 3 KNIGHT JK 21 KNIGHT JK 5.59
## 4 SMITH MK 17 [ANONYMOUS] 5.00
## 5 DOLAN EL 16 ALLEN D 4.14
## 6 ANDREWS TC 11 BRAME CJ 3.83
## 7 COUCH BA 11 SMITH MK 3.78
## 8 CORWIN LA 10 DOLAN EL 3.60
## 9 EDDY SL 10 ANDREWS TC 3.14
## 10 WENDEROTH MP 10 SCHUSSLER EE 2.68
##
##
## Top manuscripts per citations
##
## Paper TC TCperYear
## 1 AUCHINCLOSS LC, 2014, CBE-LIFE SCI EDUC 216 30.9
## 2 JENSEN JL, 2015, CBE-LIFE SCI EDUC 215 35.8
## 3 BROWNELL SE, 2012, CBE-LIFE SCI EDUC 163 18.1
## 4 ANDREWS TM, 2011, CBE-LIFE SCI EDUC 154 15.4
## 5 EDDY SL, 2014, CBE-LIFE SCI EDUC 134 19.1
## 6 MORAVEC M, 2010, CBE-LIFE SCI EDUC 130 11.8
## 7 MAHER JM, 2013, CBE-LIFE SCI EDUC 127 15.9
## 8 TANNER KD, 2012, CBE-LIFE SCI EDUC 125 13.9
## 9 SMITH MK, 2013, CBE-LIFE SCI EDUC 117 14.6
## 10 SMITH MK, 2011, CBE-LIFE SCI EDUC-a 112 11.2
##
##
## Corresponding Author's Countries
##
## Country Articles Freq SCP MCP MCP_Ratio
## 1 USA 638 0.92464 615 23 0.0361
## 2 CANADA 16 0.02319 13 3 0.1875
## 3 AUSTRALIA 4 0.00580 4 0 0.0000
## 4 NETHERLANDS 4 0.00580 4 0 0.0000
## 5 SWEDEN 4 0.00580 2 2 0.5000
## 6 GERMANY 3 0.00435 2 1 0.3333
## 7 ISRAEL 2 0.00290 2 0 0.0000
## 8 NEW ZEALAND 2 0.00290 1 1 0.5000
## 9 NORWAY 2 0.00290 2 0 0.0000
## 10 UNITED KINGDOM 2 0.00290 2 0 0.0000
##
##
## SCP: Single Country Publications
##
## MCP: Multiple Country Publications
##
##
## Total Citations per Country
##
## Country Total Citations Average Article Citations
## 1 USA 9534 14.94
## 2 CANADA 127 7.94
## 3 NETHERLANDS 79 19.75
## 4 SWEDEN 78 19.50
## 5 AUSTRALIA 60 15.00
## 6 ISRAEL 18 9.00
## 7 SLOVENIA 14 14.00
## 8 CHINA 13 13.00
## 9 CZECH REPUBLIC 13 13.00
## 10 FRANCE 13 13.00
##
##
## Most Relevant Sources
##
## Sources Articles
## 1 CBE-LIFE SCIENCES EDUCATION 708S## $MainInformation
## [1] "\n\nMAIN INFORMATION ABOUT DATA\n\n" "Timespan 2010 : 2019 \n"
## [3] "Sources (Journals, Books, etc) 1 \n" "Documents 708 \n"
## [5] "Average years from publication 4.97 \n" "Average citations per documents 14.12 \n"
## [7] "Average citations per year per doc 2.044 \n" "References 18276 \n"
## [9] "\nDOCUMENT TYPES \n" "article 656 \n"
## [11] "correction 18 \n" "letter 30 \n"
## [13] "review 4 \n" "\nDOCUMENT CONTENTS\n"
## [15] "Keywords Plus (ID) 983 \n" "Author's Keywords (DE) 0 \n"
## [17] "\nAUTHORS\n" "Authors 2295 \n"
## [19] "Author Appearances 3255 \n" "Authors of single-authored documents 59 \n"
## [21] "Authors of multi-authored documents 2236 \n" "\nAUTHORS COLLABORATION\n"
## [23] "Single-authored documents 70 \n" "Documents per Author 0.308 \n"
## [25] "Authors per Document 3.24 \n" "Co-Authors per Documents 4.6 \n"
## [27] "Collaboration Index 3.5 \n" "\n"
##
## $MainInformationDF
## Description Results
## 1 MAIN INFORMATION ABOUT DATA
## 2 Timespan 2010:2019
## 3 Sources (Journals, Books, etc) 1
## 4 Documents 708
## 5 Average years from publication 4.97
## 6 Average citations per documents 14.12
## 7 Average citations per year per doc 2.044
## 8 References 18276
## 9 DOCUMENT TYPES
## 10 article 656
## 11 correction 18
## 12 letter 30
## 13 review 4
## 14 DOCUMENT CONTENTS
## 15 Keywords Plus (ID) 983
## 16 Author's Keywords (DE) 0
## 17 AUTHORS
## 18 Authors 2295
## 19 Author Appearances 3255
## 20 Authors of single-authored documents 59
## 21 Authors of multi-authored documents 2236
## 22 AUTHORS COLLABORATION
## 23 Single-authored documents 70
## 24 Documents per Author 0.308
## 25 Authors per Document 3.24
## 26 Co-Authors per Documents 4.6
## 27 Collaboration Index 3.5
## 28
##
## $AnnualProduction
## Year Articles
## 1 2010 59
## 2 2011 40
## 3 2012 50
## 4 2013 76
## 5 2014 71
## 6 2015 58
## 7 2016 105
## 8 2017 84
## 9 2018 84
## 10 2019 81
##
## $AnnualGrowthRate
## [1] 3.583971
##
## $MostProdAuthors
## Authors Articles Authors Articles Fractionalized
## 1 TANNER KD 23 TANNER KD 10.81
## 2 BROWNELL SE 21 BROWNELL SE 5.92
## 3 KNIGHT JK 21 KNIGHT JK 5.59
## 4 SMITH MK 17 [ANONYMOUS] 5.00
## 5 DOLAN EL 16 ALLEN D 4.14
## 6 ANDREWS TC 11 BRAME CJ 3.83
## 7 COUCH BA 11 SMITH MK 3.78
## 8 CORWIN LA 10 DOLAN EL 3.60
## 9 EDDY SL 10 ANDREWS TC 3.14
## 10 WENDEROTH MP 10 SCHUSSLER EE 2.68
##
## $MostCitedPapers
## Paper TC TCperYear
## 1 AUCHINCLOSS LC, 2014, CBE-LIFE SCI EDUC 216 30.9
## 2 JENSEN JL, 2015, CBE-LIFE SCI EDUC 215 35.8
## 3 BROWNELL SE, 2012, CBE-LIFE SCI EDUC 163 18.1
## 4 ANDREWS TM, 2011, CBE-LIFE SCI EDUC 154 15.4
## 5 EDDY SL, 2014, CBE-LIFE SCI EDUC 134 19.1
## 6 MORAVEC M, 2010, CBE-LIFE SCI EDUC 130 11.8
## 7 MAHER JM, 2013, CBE-LIFE SCI EDUC 127 15.9
## 8 TANNER KD, 2012, CBE-LIFE SCI EDUC 125 13.9
## 9 SMITH MK, 2013, CBE-LIFE SCI EDUC 117 14.6
## 10 SMITH MK, 2011, CBE-LIFE SCI EDUC-a 112 11.2
##
## $MostProdCountries
## Country Articles Freq SCP MCP MCP_Ratio
## 1 USA 638 0.92464 615 23 0.0361
## 2 CANADA 16 0.02319 13 3 0.1875
## 3 AUSTRALIA 4 0.00580 4 0 0.0000
## 4 NETHERLANDS 4 0.00580 4 0 0.0000
## 5 SWEDEN 4 0.00580 2 2 0.5000
## 6 GERMANY 3 0.00435 2 1 0.3333
## 7 ISRAEL 2 0.00290 2 0 0.0000
## 8 NEW ZEALAND 2 0.00290 1 1 0.5000
## 9 NORWAY 2 0.00290 2 0 0.0000
## 10 UNITED KINGDOM 2 0.00290 2 0 0.0000
##
## $TCperCountries
## Country Total Citations Average Article Citations
## 1 USA 9534 14.94
## 2 CANADA 127 7.94
## 3 NETHERLANDS 79 19.75
## 4 SWEDEN 78 19.50
## 5 AUSTRALIA 60 15.00
## 6 ISRAEL 18 9.00
## 7 SLOVENIA 14 14.00
## 8 CHINA 13 13.00
## 9 CZECH REPUBLIC 13 13.00
## 10 FRANCE 13 13.00
##
## $MostRelSources
## Sources Articles
## 1 CBE-LIFE SCIENCES EDUCATION 708
##
## $MostRelKeywords
## NULLplot(x=results, k=10, pause=F)
## Warning: Use of `xx$Country` is discouraged. Use `Country` instead.## Warning: Use of `xx$Freq` is discouraged. Use `Freq` instead.## Warning: Use of `xx$Collaboration` is discouraged. Use `Collaboration` instead.
## Warning: Use of `Y$Year` is discouraged. Use `Year` instead.## Warning: Use of `Y$Freq` is discouraged. Use `Freq` instead.## Warning: Use of `Y$Year` is discouraged. Use `Year` instead.## Warning: Use of `Y$Freq` is discouraged. Use `Freq` instead.
## Warning: Use of `Table2$Year` is discouraged. Use `Year` instead.## Warning: Use of `Table2$MeanTCperYear` is discouraged. Use `MeanTCperYear` instead.## Warning: Use of `Table2$Year` is discouraged. Use `Year` instead.## Warning: Use of `Table2$MeanTCperYear` is discouraged. Use `MeanTCperYear` instead.
## Warning: Use of `Table2$Year` is discouraged. Use `Year` instead.## Warning: Use of `Table2$MeanTCperArt` is discouraged. Use `MeanTCperArt` instead.## Warning: Use of `Table2$Year` is discouraged. Use `Year` instead.## Warning: Use of `Table2$MeanTCperArt` is discouraged. Use `MeanTCperArt` instead.
S$MainInformationDF## Description Results
## 1 MAIN INFORMATION ABOUT DATA
## 2 Timespan 2010:2019
## 3 Sources (Journals, Books, etc) 1
## 4 Documents 708
## 5 Average years from publication 4.97
## 6 Average citations per documents 14.12
## 7 Average citations per year per doc 2.044
## 8 References 18276
## 9 DOCUMENT TYPES
## 10 article 656
## 11 correction 18
## 12 letter 30
## 13 review 4
## 14 DOCUMENT CONTENTS
## 15 Keywords Plus (ID) 983
## 16 Author's Keywords (DE) 0
## 17 AUTHORS
## 18 Authors 2295
## 19 Author Appearances 3255
## 20 Authors of single-authored documents 59
## 21 Authors of multi-authored documents 2236
## 22 AUTHORS COLLABORATION
## 23 Single-authored documents 70
## 24 Documents per Author 0.308
## 25 Authors per Document 3.24
## 26 Co-Authors per Documents 4.6
## 27 Collaboration Index 3.5
## 28write.csv(file = "keywords.csv",as.data.frame(results$ID[1:20]))
write.csv(file = "Maininformation.csv",S$MainInformationDF)
write.csv(file = "ArticlesYears.csv",S$AnnualProduction)
write.csv(file = "MostCitedPapers.csv",S$MostCitedPapers)
write.csv(file = "MostProdAuthors.csv", S$MostProdAuthors)##Authoring
authors=gsub(","," ",names(results$Authors)[1:10])
indices <- Hindex(data, field = "author", elements=authors, sep = ";", years = 50)
indices$H## Author h_index g_index m_index TC NP PY_start
## 1 TANNER KD 11 23 1.0000000 667 23 2010
## 2 BROWNELL SE 10 21 1.1111111 566 21 2012
## 3 KNIGHT JK 9 20 0.8181818 416 21 2010
## 4 SMITH MK 9 17 0.8181818 424 17 2010
## 5 DOLAN EL 11 16 1.0000000 604 16 2010
## 6 ANDREWS TC 5 10 0.7142857 115 11 2014
## 7 COUCH BA 6 10 1.0000000 110 11 2015
## 8 CORWIN LA 5 10 0.8333333 170 10 2015
## 9 EDDY SL 7 10 1.0000000 373 10 2014
## 10 WENDEROTH MP 7 10 0.6363636 318 10 2010avgAU <- as.data.frame(cbind(results[["nAUperPaper"]],results[["Years"]]))
colnames(avgAU) <-c("nAu","Years")
avgAUres<-avgAU %>%
mutate(Years = as.factor(Years)) %>%
group_by(Years) %>%
summarize(avg = mean(nAu), med = median(nAu))## `summarise()` ungrouping output (override with `.groups` argument)lm(nAu ~ Years, avgAU %>% mutate(Years = Years - 2010))##
## Call:
## lm(formula = nAu ~ Years, data = avgAU %>% mutate(Years = Years -
## 2010))
##
## Coefficients:
## (Intercept) Years
## 3.8091 0.1567ggplot(avgAU,aes(Years,nAu)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
ggplot(avgAUres,aes(x = Years, y = med)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Research Questions
What are the collaborative connections between institutions?
Germaine Ng
This is the placeholder for the question about collaborations between different universities (institutions) Text to introduce the script and outputs
Affiliation=results$Affiliations[1:20]
Affiliation## AFF
## MICHIGAN STATE UNIV UNIV COLORADO UNIV GEORGIA UNIV WASHINGTON
## 83 67 65 58
## UNIV WISCONSIN SAN FRANCISCO STATE UNIV PURDUE UNIV UNIV CALIF LOS ANGELES
## 53 44 37 37
## WASHINGTON UNIV ARIZONA STATE UNIV UNIV BRITISH COLUMBIA DUKE UNIV
## 35 33 30 29
## UNIV NEBRASKA EMORY UNIV UNIV TEXAS AUSTIN UNIV CALIF SAN DIEGO
## 26 24 24 23
## UNIV MARYLAND UNIV MINNESOTA VANDERBILT UNIV YALE UNIV
## 23 23 23 23Aff_Freq=results[["Aff_frac"]] %>%
arrange(desc(Frequency))%>%
top_n(10)## Selecting by FrequencyAff_Freq## Affiliation Frequency
## 1 MICHIGAN STATE UNIV 24.805700
## 2 UNIV COLORADO 23.977510
## 3 UNIV GEORGIA 22.598521
## 4 UNIV WASHINGTON 21.628175
## 5 SAN FRANCISCO STATE UNIV 17.965443
## 6 ARIZONA STATE UNIV 14.793254
## 7 UNIV WISCONSIN 14.505231
## 8 PURDUE UNIV 10.394051
## 9 UNIV BRITISH COLUMBIA 10.083333
## 10 UNIV MINNESOTA 9.596825## Warning in closeness(bsk.network): At centrality.c:2784 :closeness centrality is not well-defined for disconnected
## graphs
## san francisco state univ washington univ univ colorado grinnell coll
## 342 266 222 219
## univ calif san diego duke univ
## 214 210## Warning in closeness(bsk.network): At centrality.c:2784 :closeness centrality is not well-defined for disconnected
## graphs
What is the intellectual structures? Document co-citation network and topic analysis
Darcie Nelson
library(stringdist)##
## Attaching package: 'stringdist'## The following object is masked from 'package:tidyr':
##
## extractlibrary(stringr)
library(stringi)
library(plyr)## ----------------------------------------------------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## ----------------------------------------------------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise, summarize## The following object is masked from 'package:purrr':
##
## compactlibrary(dplyr)
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:stringdist':
##
## extract## The following object is masked from 'package:purrr':
##
## set_names## The following object is masked from 'package:tidyr':
##
## extractlibrary(broom)
library(lazyeval)##
## Attaching package: 'lazyeval'## The following objects are masked from 'package:purrr':
##
## is_atomic, is_formulalibrary(tidyr)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithslibrary(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:reshape2':
##
## dcast, melt## The following objects are masked from 'package:dplyr':
##
## between, first, last## The following object is masked from 'package:purrr':
##
## transpose#citations
# load functions
source("cleaningfunctions/citation_functions.R")
# Extract citations from WOS list
work_data <- as.data.frame(extract_citation(data, "CR"))## Warning in melt(citations): The melt generic in data.table has been passed a list and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is deprecated, and this redirection is now deprecated as well.
## To continue using melt methods from reshape2 while both libraries are attached, e.g. melt.list, you can prepend the
## namespace like reshape2::melt(citations). In the next version, this warning will become an error. #work_data$label <- as.character(work_data$label)
work_data$L1<- as.factor(work_data$L1)
work_data$index<-as.factor( work_data$index)
# Standardize the data--------------------------------------------------------------------
std_data <- standardize(work_data)
# show authors who are longer than 50 characters
author_temp <- std_data %>%
select(authors) %>%
filter(nchar(authors) > 50)
# clean and standardize author names -----------------------------------------------------
# Run fuzzy match function on a .2 threshold
std_data$authorBlock <- fuzzy_match(std_data$authors, .1, std_data$authors)
# look at author clusters
author_merges <- std_data %>%
group_by(authorBlock) %>%
dplyr::summarise(count = n()) %>%
filter(count > 15) %>%
arrange(desc(count))## `summarise()` ungrouping output (override with `.groups` argument) # Run the clustering algorithm again to recluster top merge
# get the top merged author
name <- author_merges[1, ][[1]]
std_data2 <- std_data %>%
filter(authorBlock == name) %>%
mutate(authorBlock = fuzzy_match(authors, .08, authors))
std_data3 <- std_data %>%
filter(authorBlock != name) %>%
bind_rows(std_data2)
std_data4 <- std_data3 %>%
filter(authorBlock == name) %>%
mutate(authorBlock = fuzzy_match(authors, .05, authors))
std_data5 <- std_data3 %>%
filter(authorBlock != name) %>%
bind_rows(std_data4)
#Add specific criteria for problematic author names
std_data5$authorBlock[grep("assaraf", std_data5$authorBlock)]## [1] "assaraf obz" "assaraf obz" "assaraf obz" "assaraf obz"# Run clustering algorithm----------------------------------------------------------------
books <- std_data5 %>%
group_by(authorBlock, documentName, documentYear) %>%
filter(type == "book") %>%
mutate(mergedLabel = fuzzy_match(cleanLabel, .05, cleanLabel))
articles <- std_data5 %>%
group_by(authorBlock, documentName, documentYear) %>%
filter(type == "article") %>%
mutate(mergedLabel = fuzzy_match(cleanLabel, .01, cleanLabel))
clean_data <- rbind(books, articles)
# create the flat data file
flat_data<- as.data.table(clean_data)[, toString(paste0(mergedLabel,collapse = ";")), by = list(L1)]
# make sure to unwrap this again to check if it was wrapped properly
rownames(flat_data) <- as.character(flat_data$L1)
flat_data2 <- inner_join(data, flat_data, by = c("SR" = "L1"))
flat_data2$CR <- flat_data2$V1
# find the similarity of the merges------------------------------------------------------
clean_data <- clean_data %>%
mutate(jwSimilarity = calc_jw(cleanLabel, mergedLabel))
# plot------------------------------------------------------------------------------------
merged <- clean_data %>%
filter(jwSimilarity < 1)
hist(round(merged$jwSimilarity, 3),
xlab = "jw similarity distance",
main = "Freq Merges by similarity distance \n with year and document type boundary")
# save the data------------------------------------------------------------------------------------
write.csv(clean_data, "WOS_nodeList.csv", row.names = FALSE)
write.csv(flat_data2, "WOS_clean.csv", row.names = FALSE)
CR <- citations(data, field = "article", sep = ";")
CRtable <-cbind(CR$Cited[1:20])
CRtable## [,1]
## AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE, 2011, VIS CHANG UND BIOL E 159
## FREEMAN S, 2014, P NATL ACAD SCI USA, V111, P8410, DOI 10.1073/PNAS.1319030111 105
## [ANONYMOUS], 2012, ENG EXC PROD ON MILL 75
## CROWE A, 2008, CBE-LIFE SCI EDUC, V7, P368, DOI 10.1187/CBE.08-05-0024 70
## HANDELSMAN J, 2004, SCIENCE, V304, P521, DOI 10.1126/SCIENCE.1096022 62
## SMITH MK, 2008, CBE-LIFE SCI EDUC, V7, P422, DOI 10.1187/CBE.08-08-0045 62
## HAAK DC, 2011, SCIENCE, V332, P1213, DOI 10.1126/SCIENCE.1204820 54
## SEYMOUR E., 1997, TALKING LEAVING WHY 51
## FREEMAN SCOTT, 2007, CBE LIFE SCI EDUC, V6, P132, DOI 10.1187/CBE.06-09-0194 50
## EBERT-MAY D, 2011, BIOSCIENCE, V61, P550, DOI 10.1525/BIO.2011.61.7.9 49
## EDDY SL, 2014, CBE-LIFE SCI EDUC, V13, P453, DOI 10.1187/CBE.14-03-0050 49
## HAKE RR, 1998, AM J PHYS, V66, P64, DOI 10.1119/1.18809 49
## KNIGHT JENNIFER K, 2005, CELL BIOL EDUC, V4, P298, DOI 10.1187/05-06-0082 49
## SEYMOUR E, 2004, SCI EDUC, V88, P493, DOI 10.1002/SCE.10131 48
## HENDERSON C, 2011, J RES SCI TEACH, V48, P952, DOI 10.1002/TEA.20439 47
## RUSSELL SH, 2007, SCIENCE, V316, P548, DOI 10.1126/SCIENCE.1140384 47
## ANDERSON DL, 2002, J RES SCI TEACH, V39, P952, DOI 10.1002/TEA.10053 44
## LOPATTO DAVID, 2007, CBE LIFE SCI EDUC, V6, P297, DOI 10.1187/CBE.07-06-0039 44
## NATIONAL RESEARCH COUNCIL, 2003, BIO2010 TRANSF UND E 44
## AUCHINCLOSS LC, 2014, CBE-LIFE SCI EDUC, V13, P29, DOI 10.1187/CBE.14-01-0004 43CR2 <- citations(flat_data2, field = "article", sep = ";")
CR2table <-cbind(CR2$Cited[1:20])
CR2table## [,1]
## american association for the advancement of science, 2011, vis chang und biol e 185
## freeman s, 2014, p natl acad sci usa, v111, p8410 105
## anonymous, 2012, eng exc prod on mill 75
## crowe a, 2008, cbelife sci educ, v7, p368 70
## handelsman j, 2004, science, v304, p521 62
## smith mk, 2008, cbelife sci educ, v7, p422 62
## seymour e, 1997, talking leaving why 59
## haak dc, 2011, science, v332, p1213 54
## freeman scott, 2007, cbe life sci educ, v6, p132 50
## ebertmay d, 2011, bioscience, v61, p550 49
## eddy sl, 2014, cbelife sci educ, v13, p453 49
## hake rr, 1998, am j phys, v66, p64 49
## handelsman j, 2007, sci teaching 49
## knight jennifer k, 2005, cell biol educ, v4, p298 49
## seymour e, 2004, sci educ, v88, p493 48
## henderson c, 2011, j res sci teach, v48, p952 47
## national research council, 2003, bio2010 transf und e 47
## russell sh, 2007, science, v316, p548 47
## anderson dl, 2002, j res sci teach, v39, p952 44
## lopatto david, 2007, cbe life sci educ, v6, p297 44write.csv(file = "Citations.csv",CR2table)
sourcestable <-cbind(summary(factor(CR$Source))
[1:10])
sourcestable## [,1]
## CBE-LIFE SCI EDUC 561
## J RES SCI TEACH 393
## SCIENCE 263
## J COLL SCI TEACH 235
## INT J SCI EDUC 205
## SCI EDUC 203
## J CHEM EDUC 188
## J EDUC PSYCHOL 159
## AM BIOL TEACH 149
## BIOCHEM MOL BIOL EDU 110write.csv(file = "Sources.csv",sourcestable)## Warning in closeness(bsk.network): At centrality.c:2784 :closeness centrality is not well-defined for disconnected
## graphs
Social Network Analysis (SNA) of co-authorship: What social structures are organizing co-authorship?
Rachel Kudlacz This is the placeholder for the question about the social struction of co-authorship the among the CBE-LSE authors. A social network analysis approach was used… Text to introduce the script and outputs
Text to explain what is shown in the figures.